Congratulations to the undergraduate students of the Class of 2023 on successfully graduating!
Congratulations to Huang Ziang, Yao Zhiyuan, Wu Zeming, Ge Jiahao, He Weiqi, Wu Wenjie, Xu Tianyu, and Wang Yingyao on successfully graduating from Shanghai University, majoring in Computer Science and Technology! It’s wonderful to hear that your undergraduate studies have come to a successful conclusion. Additionally, it’s great to know that your graduation projects were guided by Professor Han. Best wishes to all of you as you embark on the next chapter of your lives!
Huang Ziang’s graduation project focused on the research and development of a material image database system platform. The goal was to construct a system platform that enables data uploading, data querying, and algorithm usage. This platform provides a user-friendly front-end interface for accessing and manipulating data, as well as a back-end system for data storage, querying, and algorithm invocation. The ultimate objective was to accomplish tasks such as retrieving and processing key data from individual or batch material images.
Yao Zhiyuan’s graduation project focused on the research and development of a material literature database system platform. The objective was to achieve tasks such as retrieving and processing key data from individual or batch material literature. The project aimed to develop a system platform that enables efficient searching, retrieval, and manipulation of material literature data, empowering users to access and analyze relevant information effectively.
Wu Zeming’s graduation project focused on the research and development of a street environment governance assessment system based on video processing. The project utilized deep learning algorithms for video processing and image processing research to construct the street environment governance assessment system. The system provided evaluation scores for assessing the effectiveness of street environment governance.
Ge Jiahao’s graduation project focused on the research of methods for integrating key content from different videos. The project was based on video processing and aimed to segment key objects from one video and project the visual representation of these key objects onto another video. This process enabled the fusion of content from different videos, resulting in a seamless integration of visual elements.
He Weiqi’s graduation project focused on the construction and development of a carbon fiber reinforced polymer composite material database. Considering the complexity of various data associated with carbon fiber reinforced polymer composites, efficient management was required. Given the data template, the project aimed to accomplish the construction and development of a carbon fiber reinforced polymer composite material database.
Wu Wenjie’s graduation project focused on the research of grain shape extraction and statistical methods for microstructure images of copper alloys. Taking widely used copper alloys in the electronics industry as an example, the project aimed to develop algorithms based on material image segmentation and recognition. These algorithms were used to extract quantitative descriptive features from alloy microstructure images and correlate them with mechanical and electrical properties. The project laid the foundation for performance prediction and design based on alloy material microstructure.
Xu Tianyu’s graduation project focused on the research and development of constructing an academic literature knowledge graph. The project aimed to design a tool that utilizes techniques from the field of natural language processing and machine learning algorithms. This tool would be able to automatically extract knowledge from academic literature, generate a knowledge graph, and visualize it for display purposes.
Wang Yingyao’s graduation project focused on the development of a high-concurrency memory pool in C++. The project aimed to create a high-concurrency memory pool based on the prototype of Google’s open-source project, tcmalloc. The core framework was simplified and a simplified version of the high-concurrency memory pool was simulated and implemented.